The field of the invention is contrast enhanced magnetic resonance angiography (“CEMRA”), and more particularly, a method for reconstructing CEMRA images from highly undersampled MR data.
Magnetic resonance imaging (MRI) uses the nuclear magnetic resonance (NMR) phenomenon to produce images. When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the spins in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a magnetic field (excitation field B1) which is in the x-y plane and which is near the Larmor frequency, the net aligned moment, Mz, may be rotated, or “tipped”, into the x-y plane to produce a net transverse magnetic moment Mt. A signal is emitted by the excited spins, and after the excitation signal B1 is terminated, this signal may be received and processed to form an image.
When utilizing these signals to produce images, magnetic field gradients (Gx Gy and Gz) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradients vary according to the particular localization method being used. Each measurement is referred to in the art as a “view” and the number of views determines such factors as the resolution and SNR of the image. The resulting set of received NMR signals, or views, or k-space samples, are digitized and processed to reconstruct the image using one of many well known reconstruction techniques. The total scan time is determined in part by the number of measurement cycles, or views, that are acquired for an image, and therefore, scan time can be reduced at the expense of image resolution or image signal-to-noise ratio (“SNR”) by reducing the number of acquired views.
Magnetic resonance angiography (MRA) uses nuclear magnetic resonance phenomenon to produce images of the human vasculature. Two basic MRA techniques have been proposed and evaluated. The first class, time-of-flight (TOF) techniques, consists of methods which exploit the differences in signal saturation that exist between flowing blood and stationary tissue. Flowing blood, which is moving through the excited section, is continually refreshed by spins experiencing fewer excitation pulses and is, therefore, less saturated. This effect is magnified by injecting a contrast agent into the patient and timing the acquisition when the contrast bolus flows through the arteries of interest. The result is the desired image contrast between the high-signal blood and the low-signal stationary tissues.
MR methods have also been developed that encode motion into the phase of the acquired signal as disclosed in U.S. Pat. No. Re. 32,701. These form the second class of MRA techniques and are known as phase contrast (PC) methods. Currently, most PC MRA techniques acquire two images, with each image having a different sensitivity to the same velocity component. Angiographic images are then obtained by forming either the phase difference or complex difference between the pair of velocity-encoded images. Phase contrast MRA techniques have been extended so that they are sensitive to velocity components in all three orthogonal directions, but this requires additional data acquisition.
The most prevalent method for acquiring an NMR data set from which an image can be reconstructed is referred to as the “Fourier transform” imaging technique or “spin-warp” technique. This technique is discussed in an article entitled “Spin-Warp NMR Imaging and Applications to Human Whole-Body Imaging”, by W. A. Edelstein et al., Physics in Medicine and Biology, Vol. 25, p. 751-756 (1980). It employs a variable amplitude phase encoding magnetic field gradient pulse prior to the acquisition of NMR signals to phase encode spatial information in the direction of this gradient. In a two-dimensional implementation (2DFT), for example, spatial information is encoded in one direction by applying a phase encoding gradient (Gy) along that direction, and then a signal is acquired in the presence of a readout magnetic field gradient (Gx) in a direction orthogonal to the phase encoding direction. The readout gradient present during the spin-echo acquisition encodes spatial information in the orthogonal direction. In a typical 2DFT pulse sequence, the magnitude of the phase encoding gradient pulse Gy is incremented in the sequence of views that are acquired during the scan. In a three-dimensional implementation (3DFT) a third gradient (Gz) is applied before each signal readout to phase encode along the third axis. The magnitude of this second phase encoding gradient pulse Gz is also stepped through values during the scan. These 2DFT and 3DFT methods sample k-space in a rectilinear pattern such as that shown in FIG. 2A and they require considerable scan time in order to sample k-space adequately.
There has been extensive recent work using multiple receiver coil arrays to shorten imaging scan time. In the SMASH technique described by Griswold, et al., “Simultaneous Acquisition Of Spatial Harmonics (SMASH)” Magnetic Resonance In Medicine 1999, June; 41(6):1235-45, multiple coils are carefully positioned in one of the Fourier phase encoding directions. Using knowledge of the coil sensitivities, non-acquired phase encodings can be synthesized, thus increasing the rate at which images of a given resolution can be acquired, or increasing the resolution of images acquired at the same rate. The SENSE technique described by Pruessmann et al., “Coil Sensitivity Encoding for Fast MRI”, MRM 42:952-962 (1999) is another such multiple receive channel approach to reducing scan time. The SMASH and SENSE methods are characterized by a factor “R” representing the speed increase over conventional methods on the order of 2 to 3 for a given resolution. They are also characterized by a factor “g”, on the order of 1-1.2 representing the increase in noise beyond what would be expected for a given imaging time.
There has also been recent work using projection reconstruction methods for acquiring MRI data as disclosed in U.S. Pat. No. 6,487,435. Projection reconstruction methods have been known since the inception of magnetic resonance imaging. Rather than sampling k-space in a rectilinear scan pattern as is done in Fourier imaging and shown in FIG. 2A, projection reconstruction methods sample k-space with a series of “projections” or views that sample radial lines extending outward from the center of k-space as shown in FIG. 2B. The number of views needed to sample k-space determines the length of the scan and if an insufficient number of views are acquired, streak artifacts are produced in the reconstructed image. The technique disclosed in U.S. Pat. No. 6,487,435 reduces such streaking by acquiring successive undersampled images with interleaved views and sharing peripheral k-space data between successive image frames. This method of sharing acquired peripheral k-space data is known in the art by the acronym “TRICKS”.
There are two methods used to reconstruct images from an acquired set of k-space projection views as described, for example, in U.S. Pat. No. 6,710,686. The most common method is to regrid the k-space samples from their locations on the radial sampling trajectories to a Cartesian grid. The image is then reconstructed by performing a 2D or 3D Fourier transformation of the regridded k-space samples in the conventional manner.
The second method for reconstructing an image is to transform the radial k-space projection views to Radon space by Fourier transforming each projection view. An image is reconstructed from these signal projections by filtering and backprojecting them into the field of view (FOV). As is well known in the art, if the acquired signal projections are insufficient in number to satisfy the Nyquist sampling theorem, streak artifacts will be produced in the reconstructed image.
The standard backprojection method is illustrated in FIG. 3. Each Radon space signal projection profile 11 is backprojected into the field of view 13 by projecting each signal sample 15 in the profile 11 through the FOV 13 along the projection path as indicted by arrows 17. In projecting each signal sample 15 in the FOV 13 we have no a priori knowledge of the subject and the assumption is made that the NMR signals in the FOV 13 are homogeneous and that the signal sample 15 should be distributed equally in each pixel through which the projection path passes. For example, a projection path 8 is illustrated in FIG. 3 for a single signal sample 15 in one signal projection profile 11 as it passes through N pixels in the FOV 13. The signal value (P) of this signal sample 15 is divided up equally between these N pixels in a conventional backprojection:Sn=(P×1)/N  (1)
where: Sn is the NMR signal value distributed to the nth pixel in a projection path having N pixels through the FOV 13.
Clearly, the assumption that the NMR signal in the FOV 13 is homogeneous is not correct. However, as is well known in the art, if certain corrections are made to each signal profile 11 and a sufficient number of profiles are acquired at a corresponding number of projection angles, the errors caused by this faulty assumption are minimized and image artifacts are suppressed. In a typical, filtered backprojection method of image reconstruction, 400 projections are required for a 256×256 pixel 2D image and 203,000 projections are required for a 256×256×256 pixel 3D image. If the method described in the above-cited U.S. Pat. No. 6,487,435 is employed, the number of projection views needed for these same images can be reduced to 100 (2D) and 2000 (3D).
The kt-blast technique disclosed by Tsao J., Besinger P. and Pruessman KP, “kt-Blast and k-t Sense: Dynamic MRI with High Frame Rate Exploiting Spatiotemporal Correlations”, Magn. Reson. Med. 2003 November; 50(5):1031-43, Hansen M S., Tsao J., Kozerke S., and Eggers H., “k-t Blast Reconstruction From Arbitrary k-t Sampling: Application to Dynamic Radial Imaging”, Abstract 684, 2005 ISMRM, Miami Fla., recognizes that in an acquired time series there is a great deal of correlation in the k-space data associated with an acquired set of time frames. In kt-blast, which has been applied to radial acquisitions, a low spatial frequency training data set is acquired to remove the aliasing that occurs when undersampling is performed in the spatial and temporal domains. Using iterative image reconstruction, significant reductions in the required data can be achieved.
An angiographic technique that also incorporates the idea of using a training data set to guide the reconstruction of images using pairs of orthogonal 2D projection images has been described by Huang Y., Gurr D., and Wright G., “Time-Resolved 3D MR Angiography By Interleaved Biplane Projections”, Abstract 1707, ISMRM 2005, Miami Fla. In this method an iterative image reconstruction is guided using correlation analysis of data from a training data set that is comprised of all acquired orthogonal 2D projection images.